Coin Flip

A base 50/50 chance game

Assessing statistical significance with base Luck Index calculations

To scientifically measure an individual's Luck Index, the Luckaton framework utilizes outcomes from multiple coin flips, leveraging this data as a foundation for assessing statistical significance within its comprehensive approach to quantifying luck. Here’s how Luckaton employs this methodology:

Data Collection

Players engage in a series of coin flips, recording each outcome (Heads or Tails) over a large number of trials ($N$) to gather substantial data.

Expected Probability in Coin Flips

The expected probability ($P_e​$) for obtaining either Heads or Tails in a fair coin flip is established at 0.5, reflecting the equal chance of each outcome.

Calculated Actual Probability

Luckaton calculates the actual probability ($P_a​$) of obtaining Heads (or Tails) for each participant:

$Pa​=\frac{Fa​​}{N}$

where $F_a​$ represents the frequency of obtaining Heads (or Tails).

Combined Luck Index Calculation

The Luck Index ($LI$) is derived by measuring the deviation of $P_a​$ from $P_e​$, normalized against $P_e​$, across coin flips and integrated with results from other games like Rock-Paper-Scissors and dice rolls:

$LI=​\frac{P_a​−P_e​​}{Pe}$

This formulation within the Luckaton framework quantifies luck by comparing individual outcomes against expected chance, highlighting deviations indicative of luck.

Statistical Significance

Luckaton assesses statistical significance using the Z-test for proportions, crucially applying it to the combined outcomes from all games, including coin flips, as a base:

$Z=\frac{​​Pa​−Pe​​}{\sqrt{\frac{Pe​(1−Pe​)}{N}}}$

A significant Z-score confirms that the Luck Index, and thus the observed outcomes across games, are not merely the result of random fluctuations.

In the context of statistical significance, a common threshold to determine if a result is statistically significant is a Z-score with an absolute value greater than 1.96 for a 95% confidence level. This implies that the observed result is more than 1.96 standard deviations away from the mean, which under a normal distribution corresponds to a probability (p-value) of less than 0.05, indicating less than a 5% chance that the observed outcomes are due to random variation.

Numeric Example

Suppose we are analyzing the results of a coin flip experiment to determine if a participant is luckier than average. Let's say the participant flips the coin 100 times, resulting in 60 Heads and 40 Tails.

• Expected Probability ($P_e​$) of getting Heads = 0.5 (since it's a fair coin)
• Actual Probability ($P_a​$) of getting Heads = 60/100 = 0.6
• Number of trials ($N$) = 100

$Z=\frac{​​0.6​−0.5​​}{\sqrt{\frac{0.5x(1−0.5)}{100}}}=\frac{​​0.1}{\sqrt{0.0025}}=\frac{0.1}{0.05}=2$

A Z-score of 2 means the result (60 Heads out of 100 flips) is 2 standard deviations away from the mean, exceeding the threshold of 1.96 for a 95% confidence level. This indicates the participant's outcome is statistically significant, suggesting that the observed excess of Heads is unlikely to be due to chance alone and may indicate "luck" within the Luckaton framework.

Conclusion

In the Luckaton framework, the systematic analysis of coin flip outcomes not only serves to measure luck in a binary event scenario but also lays the groundwork for a statistically robust calculation of a combined Luck Index. By integrating these results with other chance-based games, Luckaton ensures a scientifically valid and comprehensive measurement of luck, demonstrating significant deviations from chance that reflect an individual's true luck quotient.